The present invention relates to digital image evaluation and processing such as, for example, storage, transmission, and editing of digital images and evaluation of color differences.
Storage and transmission of digital images play an important role in the modern information society due to heavy use of digital images for information storage. Archives, museums, libraries, professional photographers and magazines archive their exhibits in digital form. Digital representation of an image may require large amounts of data, and hence archiving and transmission may be expensive in terms of time, computing power, bandwidth, etc. Therefore, while a desirable file format for storage of digital images should support most or all visible colors to allow visually lossless image representation, the file size should be as small as possible. Further, the file format should preferably be convenient for image editing, or allow computationally inexpensive conversion to and from a format convenient for image editing.
Image preparation for storage or transmission typically involves quantization, then decorrelation (e.g. via wavelet transform), and then compression of decorrelation coefficients (e.g. via variable length coding). Quantization involves reducing the set of color stimuli available to represent the image. FIG. 1 illustrates a typical quantization process. The set of all color stimuli is subdivided into cells 110.1, 110.2, . . . . A representative color stimulus 120.i (i=1, 2, . . . ) is defined for each cell 110.i. Typically, the color stimulus 120.i is chosen somewhere in the middle of cell 110.i. In the quantization process, any color stimulus present in a digital image and belonging to a cell 110.i is replaced with the corresponding representative color stimulus 120.i. 
Any errors introduced into the image in the quantization process should preferably be small so that image distortion would be imperceptible to a human observer (the “visual lossiness” condition). In particular, the color stimuli in each cell 110.i should be visually indistinguishable from the representative 120.i. Moreover, the representative stimuli of adjacent cells (such as cells 110.1, 110.2) should be visually indistinguishable. On the other hand, to reduce the file size, the number of cells 110.i should be as small as possible. This typically means that the cells should be as large as possible. Making the cells as large as possible allows one to discard the largest possible amount of visually unnecessary information, and this principle is sometimes called “just noticeable difference” or JND.
In a computer, the color stimuli can be represented by their coordinates in some color coordinate system (CCS) such as S1S2S3 in FIG. 1, and each coordinate S1, S2, S3 can be quantized separately. In this process, the range of values of each coordinate is subdivided into intervals, and the color stimuli's coordinates lying in such intervals are replaced with the representative values for the intervals. This quantization method can be defined in the conceptual framework of FIG. 1 if each cell 110.i is a parallelepiped formed as a set product of three intervals, and each representative stimulus 120.i has coordinates that are representative values of the respective intervals.
One such quantization method is described in the article listed as [1] in the References section below immediately before the claims. That method quantizes color stimuli defined in either the CIE XYZ color coordinate system (“CCS”) or its derivative system xyY. The color stimuli are quantized into 32-bit values, and we will refer to this method as LogLuv32. The luminance component Y is quantized into an integer value Le defined as follows:Le=└256(log2Y+64)∉  (1)The function └·┘ is the floor, i.e. if r is a real number, then └r┘ is the largest integer not exceeding r. The value Le is coded as a 16-bit integer, with one bit for the sign and 15 bits for the magnitude. According to [1], the maximum error in quantization (1) is 0.27% (i.e. 0.0027) over the entire visible gamut.
For each color stimulus xyY, the LogLuv32 method quantizes the chrominance coordinates x, y into a pair of 8-bit integer values ue, ve as follows:ue=└410u′┘  (2)ve=└410v′┘whereu′=4x/(−2x+12y+3)v′=9y/(−2x+12y+3)  (3)
Another, similar format is described in [2], but with the Y coordinate being replaced by the real luminance L, i.e.Le=└256(log2L+64)┘  (4)According to [2], the image representation (2)-(4) is perceptually uniform. In addition to this 32-bit encoding method, the reference [2] provides a 24-bit encoding method (representing each color stimulus in 24 bits).